Abstract

Abstract
In this paper we present a new fully integrated control system in a System-on-Chip (SoC) suitable to implement the controller for a Neighbourhood Electric Vehicle (NEV) based on FPGA (Field Programmable Gate Array) technology. A prototype has been designed specifically to meet the requirement of low cost and it contains all of the active functions required to implement the electronic differential. The controller uses Field Oriented Control (FOC) techniques with offline parametric identification of the motors to enhance its performance. A PC based user interface allows easy controller configuration, data acquisition and performance analysis. The functionality of the electronic differential is verified through experiments with a bench test equipment equivalent to the actual system. The experimental results show that the proposed controller is able to follow the reference torque and curvature angle with a good dynamic and relatively low error.

Abstract
The traditional knowledge discovery environment, where data and processing units are centralized in controlled laboratories and servers, is now completely transformed into a web of sensorial devices, some of them with local processing ability. This scenario represents a new knowledge-extraction environment, possibly not completely observable, that is much less controlled by both the human user and a common centralized control process. © 2007 Springer-Verlag Berlin Heidelberg.

Abstract
We study the predictive ability of some association rule measures typically used to assess descriptive interest. Such measures, namely conviction, lift and chi(2) are compared with confidence, Laplace, mutual information, cosine, Jaccard and phi-coefficient. As prediction models, we use sets of association rules. Classification is done by selecting the best rule, or by weighted voting. We performed an evaluation on 17 datasets with different characteristics and conclude that conviction is on average the best predictive measure to use in this setting. We also provide some meta-analysis insights for explaining the results.

Abstract
We describe a construction of a moduli space of solenoid functions for the C 1 +-conjugacy classes of hyperbolic dynamical systems f on surfaces with hyperbolic basic sets ?f. We explain that if the holonomies are sufficiently smooth then the diffeomorphism f is rigid in the sense that it is C 1 +conjugate to a hyperbolic affine model. We present a moduli space of measure solenoid functions for all Lipschitz conjugacy classes of C 1 +- hyperbolic dynamical systems f which have a invariant measure that is absolutely continuous with respect to Hausdorff measure. We extend Livšic and Sinai’s eigenvalue formula for Anosov diffeomorphisms which preserve an absolutely continuous measure to hyperbolic basic sets on surfaces which possess an invariant measure absolutely continuous with respect to Hausdorff measure. Introduction We say that (f, ?) is a C 1 +hyperbolic diffeomorphism if it has the following properties: (i) f: M ? M is a C 1 + adiffeomorphism of a compact surface M with respect to a C 1 + astructure on M, for some a > 0. (ii) ? is a hyperbolic invariant subset of M such that f|? is topologically transitive and ? has a local product structure. We allow both the case where ? = M and the case where ? is a proper subset of M. If ? = M then f is Anosov and M is a torus. Examples where ? is a proper subset of M include the Smale horseshoes and the codimension one attractors such as the Plykin and derived-Anosov attractors. © Mathematical Sciences Research Institute 2007.

Abstract
This is an extended analysis of eight different algorithms for musical tempo extraction and beat tracking. The algorithms participated in the 2006 Music Information Retrieval Evaluation eXchange (MIREX), where they were evaluated using a set of 140 musical excerpts, each with beats annotated by 40 different listeners. Performance metrics were constructed to measure the algorithms' abilities to predict the most perceptually salient musical beats and tempi of the excerpts. Detailed results of the evaluation are presented here and algorithm performance is evaluated as a function of musical genre, the presence of percussion, musical meter and the most salient perceptual tempo of each excerpt.